The Large Vision-Language Model (LVLM) field has seen significant
advancements, yet its progression has been hindered by challenges in
comprehending fine-grained visual content due to limited resolution. Recent
efforts have aimed to enhance the high-resolution understanding capabilities of
LVLMs, yet they remain capped at approximately 1500 x 1500 pixels and
constrained to a relatively narrow resolution range. This paper represents
InternLM-XComposer2-4KHD, a groundbreaking exploration into elevating LVLM
resolution capabilities up to 4K HD (3840 x 1600) and beyond. Concurrently,
considering the ultra-high resolution may not be necessary in all scenarios, it
supports a wide range of diverse resolutions from 336 pixels to 4K standard,
significantly broadening its scope of applicability. Specifically, this
research advances the patch division paradigm by introducing a novel extension:
dynamic resolution with automatic patch configuration. It maintains the
training image aspect ratios while automatically varying patch counts and
configuring layouts based on a pre-trained Vision Transformer (ViT) (336 x
336), leading to dynamic training resolution from 336 pixels to 4K standard.
Our research demonstrates that scaling training resolution up to 4K HD leads to
consistent performance enhancements without hitting the ceiling of potential
improvements. InternLM-XComposer2-4KHD shows superb capability that matches or
even surpasses GPT-4V and Gemini Pro in 10 of the 16 benchmarks. The
InternLM-XComposer2-4KHD model series with 7B parameters are publicly available
at this https URL

InternLM-XComposer2-4KHD 是一项具有里程碑意义的研究，通过引入动态分辨率和自动分片配置的新方法，将 LVLM 的分辨率能力提升到 4K HD 并在多个方面取得了卓越的性能提升。